%0 Journal Article 
%A Quintana Velázquez, Fernando Manuel
%A Pérez Peña, Fernando
%A Galindo Riaño, Pedro Luis
%T Bio-plausible digital implementation of a reward modulated STDP synapse
%D 2022
%@ 0941-0643

%U http://hdl.handle.net/10498/26691
%X Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP). Combining the advantages of reinforcement learning and the biological plausibility of STDP, online learning on SNN in real-world scenarios can be applied. This paper presents a fully digital architecture, implemented on an Field-Programmable Gate Array (FPGA), including the R-STDP learning mechanism in a SNN. The hardware results obtained are comparable to the software simulations results using the Brian2 simulator. The maximum error is of 0.083 when a 14-bits fix-point precision is used in realtime. The presented architecture shows an accuracy of 95% when tested in an obstacle avoidance problem on mobile robotics with a minimum use of resources.
%K R-STDP
%K STDP
%K Synaptic plasticity
%K Neuromorphic system
%K FPGA
%K Spiking neural network
%~ Universidad de Cádiz